Class optimization

lib/averages/Pwma.cs

@@ -1,5 +1,4 @@
-using System;
-using System.Linq;
+using System.Runtime.CompilerServices;
 namespace QuanTAlib;
 
 /// <summary>
@@ -31,6 +30,7 @@ public class Pwma : AbstractBase
 {
     private readonly int _period;
     private readonly Convolution _convolution;
+    private readonly double[] _kernel;
 
     /// <param name="period">The number of data points used in the PWMA calculation.</param>
     /// <exception cref="ArgumentException">Thrown when period is less than 1.</exception>
@@ -38,10 +38,11 @@ public class Pwma : AbstractBase
     {
         if (period < 1)
         {
-            throw new ArgumentException("Period must be greater than or equal to 1.", nameof(period));
+            throw new System.ArgumentException("Period must be greater than or equal to 1.", nameof(period));
         }
         _period = period;
-        _convolution = new Convolution(GenerateKernel(_period));
+        _kernel = GenerateKernel(_period);
+        _convolution = new Convolution(_kernel);
         Name = "Pwma";
         WarmupPeriod = period;
         Init();
@@ -55,12 +56,14 @@ public class Pwma : AbstractBase
         pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
     }
 
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
     private new void Init()
     {
         base.Init();
         _convolution.Init();
     }
 
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
     protected override void ManageState(bool isNew)
     {
         if (isNew)
@@ -70,24 +73,33 @@ public class Pwma : AbstractBase
         }
     }
 
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
+    private static double CalculateKernelSum(double[] kernel, int length)
+    {
+        double sum = 0;
+        for (int i = 0; i < length; i++)
+        {
+            sum += kernel[i];
+        }
+        return sum;
+    }
+
     protected override double Calculation()
     {
         ManageState(Input.IsNew);
 
         // Use Convolution for calculation
-        TValue convolutionResult = _convolution.Calc(Input);
-
+        var convolutionResult = _convolution.Calc(Input);
         double result = convolutionResult.Value;
 
         // Adjust for partial periods during warmup
         if (_index < _period)
         {
             double[] partialKernel = GenerateKernel(_index);
-            result /= partialKernel.Sum();
+            result *= CalculateKernelSum(_kernel, _period) / CalculateKernelSum(partialKernel, _index);
         }
 
         IsHot = _index >= WarmupPeriod;
-
         return result;
     }
 
@@ -96,11 +108,13 @@ public class Pwma : AbstractBase
     /// </summary>
     /// <param name="period">The period for which to generate the kernel.</param>
     /// <returns>An array of normalized Pascal's triangle-based weights for the convolution operation.</returns>
+    [MethodImpl(MethodImplOptions.AggressiveInlining)]
     public static double[] GenerateKernel(int period)
     {
         double[] kernel = new double[period];
         kernel[0] = 1;
 
+        // Generate Pascal's triangle coefficients
         for (int i = 1; i < period; i++)
         {
             for (int j = i; j > 0; j--)
@@ -109,11 +123,13 @@ public class Pwma : AbstractBase
             }
         }
 
-        // Normalize the kernel
-        double weightSum = kernel.Sum();
+        // Calculate sum and normalize in one pass
+        double weightSum = CalculateKernelSum(kernel, period);
+        double invWeightSum = 1.0 / weightSum;
+
         for (int i = 0; i < period; i++)
         {
-            kernel[i] /= weightSum;
+            kernel[i] *= invWeightSum;
         }
 
         return kernel;